Ridgeline chart

We need to load the following packages:

library(ggridges)

library(here)
library(tidyverse)

The chart we are set to recreate, originally obtained from Meta-Research: Tracking the popularity and outcomes of all bioRxiv preprints.

First, we import the data timeframe in R:

library(readxl)
timeframe <- read_excel(here("data", "timeframe.xlsx"))

str(timeframe)

tibble [7,653 × 3] (S3: tbl_df/tbl/data.frame)
 $ id      : num [1:7653] 2967 8845 16624 18517 24519 ...
 $ interval: num [1:7653] 1312 1113 1051 1045 936 ...
 $ journal : chr [1:7653] "PeerJ" "Nucleic Acids Research" "Journal of Neuroscience" "PeerJ" ...

color1 = '#9977ff'
color2 = '#5a23ff'

ggplot(timeframe, aes(
  x=interval,
  y=reorder(journal, interval, FUN=median),
  fill=reorder(journal, interval, FUN=median),
  rel_min_height=0.000000000001
)) +
  # Adding "stat = 'density' means the bandwidth (i.e. bin size)
  # is calculated separately for each journal, rather than for the
  # dataset as a whole
  stat_density_ridges(
    scale = 2,
    quantile_lines = TRUE,
    quantiles = 2
  ) +
  scale_fill_cyclical(values=c(color1, color2)) +
  scale_y_discrete(expand = c(0.01, 0.1)) +
  scale_x_continuous(
    expand = c(0.01, 0),
    breaks=seq(0, 1000, 250),
  ) +
  coord_cartesian(xlim=c(0,1000)) +
  labs(x='Age of preprint at publication (days)', y='Journal (preprint density)') +
  geom_vline(
    xintercept=166,
    col="yellow", linetype="dashed", linewidth=1
  ) +
  theme_ridges() +
  theme_bw()

Picking joint bandwidth of 32.5